This note investigates a novel cooperative design for path following of the mixed-order underactuated surface vehicle (USV) and unmanned aerial vehicle (UAV) in presence of the structure uncertainties and external disturbances. The designed cooperative scheme is comprised of the 3 dimension (3D) mapping guidance and adaptive fuzzy control algorithm. The 3D mapping guidance is developed to provide the reference signals of the yaw degree of freedom for the USV and UAV by utilizing the equivalent mapping technique. To control the USV-UAV converge to the desired path, the adaptive fuzzy control algorithm is designed for the position and attitude loops by fusing the dynamic surface control (DSC) and the Backstepping techniques. The position loop can generate the thrust inputs for the USV-UAV and provide the desired rolling and pitching angles for the UAV by using nonlinear decoupling method. In the proposed algorithm, the model uncertainties are approximated by the fuzzy logic system and only eight norm-based adaptive parameters are required to be updated online. Based on the Lyapunov theorem, the stability of the position loop and the attitude loop for the USV-UAV systems is achieved and all signals in the cooperative systems are the semi-global uniform ultimate bounded (SGUUB). The superiorities and the effectiveness of the proposed strategy are verified through the numerical experiment on the MATLAB platform under the external disturbances.

The sensor faults and the communication burden are the core issues in fields of the intelligent maritime search control. In this paper, a robust adaptive event-triggered control strategy is presented for the underactuated surface vessel-unmanned aerial vehicles (USV-UAVs) cooperative system to implement the maritime parallel search mission. The proposed scheme is comprised of two parts, i.e., the three-dimensional (3D) search guidance principle and the cooperative formation control law. The developed guidance principle can generate the reference signals for the USV and UAVs, which the maneuvering characteristics of the heterogeneous agents are considered at the waypoints. Linked with the guidance term, a robust adaptive event-triggered control algorithm is designed for the cooperative system by fusing the dynamic event-triggered mechanism and sensor-tolerant technique. The dynamic triggered threshold is constructed on basis of the state error rather than the predefined parameters. Besides, the constrains of the sensor faults and the model uncertainties are tackled by constructing the adaptive parameter and robust neural damping term. Through the Lyapunov theorem, the semi-global uniform ultimate bounded (SGUUB) stability is guaranteed for all state variables. Finally, the advantages of the proposed scheme are evaluated on simulation platform, exhibiting the good tracking accuracy and tolerant performance in presence of the external disturbances.

This article studies the model-based event-triggered control (ETC) for the tracking activity of the underactuated surface vessel (USV). Following this ideology, the continuous acquisition of states is no longer needed, and the communication traffic is reduced in the channel of sensor to controller. The control laws are fabricated in the frame of an adaptive model, which is renewed with the states of the original system whenever the triggering condition is violated. In the scheme, both internal and external uncertainties are approximated by the neural networks (NNs). To decrease the computing complexity, the minimum learning parameters (MLPs) are involved both in the adaptive model and the derived controller. The adaptive laws of only two MLPs are devised, and their updating only happens at triggering instants. Using the MLPs, an adaptive triggering condition is further derived. To avoid the ``Zeno'' phenomenon in small tracking errors, a dead-zone operator is designed for the triggering condition. Furthermore, we incorporate the dynamic surface control (DSC) into the controller design, such that the jumping of virtual control laws at triggering instants is smoothed and the problem of ``complexity explosion'' is circumvented. Through the techniques of the impulsive dynamic system and the direct Lyapunov function, the parameter setting for the DSC is derived to guarantee the semiglobal uniformly ultimate boundedness (SGUUB) of all the error signals in the closed-loop system. Finally, the effectiveness of the proposed scheme is validated through the simulation.

This article presents a novel composite neural learning fault-tolerant algorithm to implement the path-following activity of underactuated vehicles with event-triggered input. With the input event-triggered mechanism, the dominant superiority is to reduce the communication burden in the channel from the controller to actuators. In the proposed scheme, the system uncertainties are dealt with in the fusion of the neural networks (NNs) and the dynamic surface control (DSC) method. The serial-parallel estimation model (SPEM) is constructed to estimate the error dynamics, where the derived prediction error could improve the compensation effect of the NNs. As for the gain uncertainties and the unknown actuator faults, four adaptive parameters are designed to stabilize the related perturbation and not be affected by the triggering instants. Based on the direct Lyapunov theorem, considerable efforts have been made to guarantee the semiglobal uniformly ultimately bounded (SGUUB) stability of the closed-loop system. Finally, comparison and practical experiments are illustrated to verify the superiority of the proposed algorithm.